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The rise of large language models (LLMs) has driven significant progress in medical applications, including traditional Chinese medicine (TCM). However, current medical LLMs struggle with TCM diagnosis and syndrome differentiation due to substantial differences between TCM and modern medical theory, and the scarcity of specialized, high-quality corpora. This paper addresses these challenges by proposing BianCang, a TCM-specific LLM, using a two-stage training process that first injects domain-specific knowledge and then aligns it through targeted stimulation. To enhance diagnostic and differentiation capabilities, we constructed pre-training corpora, instruction-aligned datasets based on real hospital records, and the ChP-TCM dataset derived from the Pharmacopoeia of the People's Republic of China. We compiled extensive TCM and medical corpora for continuous pre-training and supervised fine-tuning, building a comprehensive dataset to refine the model's understanding of TCM. Evaluations across 11 test sets involving 29 models and 4 tasks demonstrate the effectiveness of BianCang, offering valuable insights for future research. Code, datasets, and models are available at https://github.com/QLU-NLP/BianCang.

Urban spatial interactions are a complex aggregation of routine visits and random explorations by individuals. The inherent uncertainty of these random visitations poses significant challenges to understanding urban structures and socioeconomic developments. To capture the core dynamics of urban interaction networks, we analyze the percolation structure of the $k$-most frequented destinations of intracity place-to-place flows from mobile phone data of eight major U.S. cities at a Census Block Group (CBG) level. Our study reveals a consistent percolation transition at $k^* = 130$, a critical threshold for the number of frequently visited destinations necessary to maintain a cohesive urban network. This percolation threshold proves remarkably consistent across diverse urban configurations, sizes, and geographical settings over a 48-month study period, and can largely be interpreted as the joint effect of the emergence of hubness and the level of mixing of residents. Furthermore, we examine the socioeconomic profiles of residents from different origin areas categorized by the fulfillment level of $k^*=130$ principal destinations, revealing a pronounced distinction in the origins' socioeconomic advantages. These insights offer a nuanced understanding of how urban spaces are interconnected and the determinants of travel behavior. Our findings contribute to a deeper comprehension of the structural dynamics that govern urban spatial interactions.

The deliberative potential of online platforms has been widely examined. However, little is known about how various interface-based reflection nudges impact the quality of deliberation. This paper presents two user studies with 12 and 120 participants, respectively, to investigate the impacts of different reflective nudges on the quality of deliberation. In the first study, we examined five distinct reflective nudges: persona, temporal prompts, analogies and metaphors, cultural prompts and storytelling. Persona, temporal prompts, and storytelling emerged as the preferred nudges for implementation on online deliberation platforms. In the second study, we assess the impacts of these preferred reflectors more thoroughly. Results revealed a significant positive impact of these reflectors on deliberative quality. Specifically, persona promotes a deliberative environment for balanced and opinionated viewpoints while temporal prompts promote more individualised viewpoints. Our findings suggest that the choice of reflectors can significantly influence the dynamics and shape the nature of online discussions.

The spatial arrangement of taxi hotspots indicates their inherent distribution relationships, reflecting spatial organization structure and has received attention in urban studies. Previous studies mainly explore large-scale hotspots by visual analysis or simple indexes, where hotspots usually cover the entire central business district, train stations, or dense residential areas, reaching a radius of hundreds or even thousands of meters. However, the spatial arrangement patterns of small-scale hotspots, reflecting the specific popular pick-up and drop-off locations, have not received much attention. This study quantitatively examines the spatial arrangement of fine-grained local hotspots in Wuhan and Beijing, China, using taxi trajectory data. Hotspots are adaptatively identified with sizes of 90m*90m in Wuhan and 105m*105m in Beijing according to identification method. Findings show popular hotspots are typically surrounded by less popular ones, though regions with many popular hotspots inhibit the presence of less popular ones. We term these configurations as hierarchical accompany and inhibiting patterns. Finally, inspired by both patterns, a KNN-based model is developed to describe these relationships, successfully reproducing the spatial distribution of less popular hotspots based on the most popular ones. These insights enhance understanding of local urban structures and support urban planning.

This report first provides a brief review of various forms of dialogue-based participation, e.g., Citizen Assembly, Citizen Lottery, Citizen Jury, Deliberative Polling, and Participatory Budgeting. Challenges associated with these long-lasting practices are identified and hybrid deliberation is proposed as a concept to address the challenges. The report then analyzes six leading examples of digital or hybrid formats of citizen dialogues. Through the comparison of the cases, the report concludes about the hurdles/risks, success factors/opportunities, and best practices for a complementary use of digital and analogue participation formats. Hybrid deliberation is proposed to be the future direction for dialogue-based participation that involves masses and generates high-quality outcomes.

By summarizing longer consumer health questions into shorter and essential ones, medical question answering (MQA) systems can more accurately understand consumer intentions and retrieve suitable answers. However, medical question summarization is very challenging due to obvious distinctions in health trouble descriptions from patients and doctors. Although existing works have attempted to utilize Seq2Seq, reinforcement learning, or contrastive learning to solve the problem, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance. To address these challenges, this paper proposes a novel medical question summarization framework using entity-driven contrastive learning (ECL). ECL employs medical entities in frequently asked questions (FAQs) as focuses and devises an effective mechanism to generate hard negative samples. This approach forces models to pay attention to the crucial focus information and generate more ideal question summarization. Additionally, we find that some MQA datasets suffer from serious data leakage problems, such as the iCliniq dataset's 33% duplicate rate. To evaluate the related methods fairly, this paper carefully checks leaked samples to reorganize more reasonable datasets. Extensive experiments demonstrate that our ECL method outperforms state-of-the-art methods by accurately capturing question focus and generating medical question summaries. The code and datasets are available at https://github.com/yrbobo/MQS-ECL., Comment: 16 pages, 4 figures

Existing literature on digital signage is growing but has not always emphasized the cognitive processes of the audience. This research aims to address this gap by studying how young women in Singapore cognize commercial digital signage in shopping malls and what cause them to do so. Using cognitive ethnography and taking the situated action approach, our findings suggest a comprehensive list of factors, both external and internal, that influence young women's cognition of commercial digital signage in both positive and negative ways. The research's practical implications are discussed., Comment: A previous version of the paper was presented in June 2016 at the 66th Annual Conference of the International Communication Association, Fukuoka, Japan

Guided by the Persuasion Knowledge Model and the Attribution Theory, this study investigates the perceived source expertise-product attribute congruence and sponsorship disclosure as pertinent factors affecting the communicative effectiveness of influencers. Instagram, with an immense influencer market value projected at USD2.3 billion in 2020, was chosen as the platform context. The study utilised a 2 (source expertise) x2 (product category) x2 (sponsorship disclosure) experiment to examine the roles of source-product congruence and sponsorship disclosure in affecting consumers' perception of extrinsic and intrinsic source motives, consumer resistance and ultimately, advertising effectiveness. Results revealed that the presence of a sponsorship disclosure generated stronger perceptions of extrinsic source motives but did not impact consumer resistance and advertising effectiveness, indicating that the activation of consumers' conceptual persuasion knowledge may not necessarily affect attitudinal persuasion knowledge. Source-product congruence, on the other hand, had main impacts on intrinsic motives, consumer resistance and ad effectiveness. In addition, hierarchical multiple regressions found that source-product congruence triggers a multi-stage process where consumers' perception of intrinsic source motives mediates consumer resistance which subsequently, mediates the relationship between source-product congruence and ad effectiveness.

Simulating urban morphology with location attributes is a challenging task in urban science. Recent studies have shown that Generative Adversarial Networks (GANs) have the potential to shed light on this task. However, existing GAN-based models are limited by the sparsity of urban data and instability in model training, hampering their applications. Here, we propose a GAN framework with geographical knowledge, namely Metropolitan GAN (MetroGAN), for urban morphology simulation. We incorporate a progressive growing structure to learn hierarchical features and design a geographical loss to impose the constraints of water areas. Besides, we propose a comprehensive evaluation framework for the complex structure of urban systems. Results show that MetroGAN outperforms the state-of-the-art urban simulation methods by over 20% in all metrics. Inspiringly, using physical geography features singly, MetroGAN can still generate shapes of the cities. These results demonstrate that MetroGAN solves the instability problem of previous urban simulation GANs and is generalizable to deal with various urban attributes.

There have been initiatives that take advantage of information and communication technologies to serve civic purposes, referred to as civic technologies (Civic Tech). In this paper, we present a review of 224 papers from the ACM Digital Library focusing on Computer Supported Cooperative Work and Human-Computer Interaction, the key fields supporting the building of Civic Tech. Through this review, we discuss the concepts, theories and history of civic tech research and provide insights on the technological tools, social processes and participation mechanisms involved. Our work seeks to direct future civic tech efforts to the phase of by the citizens., Comment: 33 pages, 12 figures

Learning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks to a large extent in that edges in networks propagate noises through the whole network instead of only the node itself. While existing methods tend to focus on preserving structural properties, the robustness of the learned representations against noises is generally ignored. In this paper, we propose a novel framework to learn noise-free node representations and eliminate noises simultaneously. Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting. On the one hand, the graph generator serves as a unified scheme to incorporate any useful graph prior knowledge to generate normal structures. We illustrate the generative process with community structures and power-law degree distributions as examples. On the other hand, the noise generator generates graph noises not only satisfying some fundamental properties but also in an adaptive way. Thus, real noises with arbitrary distributions can be handled successfully. Finally, in order to eliminate noises and obtain noise-free node representations, two generators need to be optimized jointly, and through maximum likelihood estimation, we equivalently convert the model into imposing different regularization constraints on the true graph and noises respectively. Our model is evaluated on both real-world and synthetic data. It outperforms other strong baselines for node classification and graph reconstruction tasks, demonstrating its ability to eliminate graph noises., Comment: 6 pages, 3 figures, ICDM 2020

Cognitive load is a significant challenge to users for being deliberative. Interface design has been used to mitigate this cognitive state. This paper surveys literature on the anchoring effect, partitioning effect and point-of-choice effect, based on which we propose three interface nudges, namely, the word-count anchor, partitioning text fields, and reply choice prompt. We then conducted a 2*2*2 factorial experiment with 80 participants (10 for each condition), testing how these nudges affect deliberativeness. The results showed a significant positive impact of the word-count anchor. There was also a significant positive impact of the partitioning text fields on the word count of response. The reply choice prompt showed a surprisingly negative affect on the quantity of response, hinting at the possibility that the reply choice prompt induces a fear of evaluation, which could in turn dampen the willingness to reply., Comment: CHI 2020, 10 pages

Laser-induced electron tunneling underlies numerous emerging spectroscopic techniques to probe attosecond electron dynamics in atoms and molecules. The improvement of those techniques requires an accurate knowledge of the exit momentum for the tunneling wave packet. Here we demonstrate a photoelectron interferometric scheme to probe the electron momentum longitudinal to the tunnel direction at the tunnel exit by measuring the photoelectron holographic pattern in an orthogonally polarized two-color laser pulse. In this scheme, we use a perturbative 400-nm laser field to modulate the photoelectron holographic fringes generated by a strong 800-nm pulse. The fringe shift offers a direct experimental access to the intermediate canonical momentum of the rescattering electron, allowing us to reconstruct the momentum offset at the tunnel exit with high accuracy. Our result unambiguously proves the existence of nonzero initial longitudinal momentum at the tunnel exit and provides fundamental insights into the non-quasi-static nature of the strong-field tunneling., Comment: 6 pages, 4 figures, accepted in Phys. Rev. Lett